Unsettling Knowledge Inequities Knowledge Equity Lab

In the final episode of our third season, we are joined by Chris Gilliard, a professor and scholar  who is highly regarded for his critiques of surveillance technology, privacy, and the invisible but problematic ways that digital technologies intersect with race, social class and marginalized communities.  
In particular, Chris’ work highlights the discriminatory practices that algorithmic decision-making enables - especially as these apply  in the higher education context. We discuss the various problems that surveillance technology and AI pose for higher education and the future of research, scholarship and academic publishing.

One of the key themes that intersects across all of our episodes this season is the surveillance and highly extractive and harmful economic practices of big corporations in the academic publishing sector, whose artificial intelligence tools are creating new forms of control and governance over our daily and professional activities.
In this episode, we are joined by Christine Cooper, Yves Gendron, and Jane Andrew - co-editors of the Critical Perspectives on Accounting journal and co-authors of the article: “The perils of artificial intelligence in academic publishing.”
We reflect on how automated decision making algorithms are deployed in academic publishing, particularly for peer review and related editorial decision making - and explore the implications of these technologies on research practices, scholarly expertise and autonomy, and the struggle for control over the future of “sustainability, creativity, and critical values of the academic world.”

Over the past 20 years, the academic publishing market has undergone changes that have led us to a juncture where power is concentrated in the hands of a handful of big companies.
To help us understand how this came to be and its implications, we are joined today by Claudio Aspesi, a leading market analyst for the academic publishing market. Claudio is a consultant at SPARC, and has authored several reports about the market power and consolidation of the largest commercial players in this space.

Resources:

SPARC Landscape Analysis: The Changing Academic Publishing Industry : Implications for Academic Institutions
https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1100&context=scholcom 

Over the last years, as the process of conducting research and scholarship has moved more and more online,  it has become clear that user surveillance and data extraction has crept into academic infrastructure in multiple ways. 
For those committed to preserving academic freedom and knowledge equity, it's important to interrogate the practices and structures of the companies that are collecting and selling this data, and the impacts of this business model on academic infrastructure - and particularly on already marginalized and underfunded scholars and students. 
To help us understand this landscape and its implications, today we are in conversation with Sarah Lamdan, author of the forthcoming book  Data Cartels: The Companies That Control and Monopolize Our Information. 

In our third season, we continue our goal of interrogating the politics of knowledge production, exchange and circulation - but with a special focus on exploring the implications of the widespread and often uncritical use of Artificial Intelligence and Machine Learning technologies.  In particular we will examine how the use of these technologies by corporate publishers and data analytics companies can replicate and exacerbate existing structural and other forms of inequities in societies and in academia.
In this first episode, we are joined by colleagues from the Distributed AI Research Institute - Dr. Alex Hannah, Dylan Baker, and Dr. Milagros Miceli.
 
DAIR is an interdisciplinary and globally distributed organization rooted in the belief that AI is not inevitable, its harms are preventable, and when its production and deployment include more diverse perspectives and more deliberate processes, it can be beneficial.

Resources mentioned in episode:
https://www.gapminder.org/dollar-street https://people.clarkson.edu/~jmatthew/publications/Wali_ParticipatoryML_ICML2020.pdfhttps://logicmag.io/beacons/the-oversight-bloc/
Other relevant resources: 
Datasets have Worldviews https://pair.withgoogle.com/explorables/dataset-worldviews/Measuring Diversity https://pair.withgoogle.com/explorables/measuring-diversity/Milagros Miceli, Tianling Yang, Adriana Alvarado Garcia, Julian Posada, Sonja MeiWang, Marc Pohl, and Alex Hanna. 2022. Documenting Data Production Processes: A Participatory Approach for Data Work. Proc. ACM Hum.-Comput. Interact. 6, CSCW2 (August 2022), 34 pages.https://arxiv.org/abs/2207.04958Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723

In the last episode of our second season we are in conversation with two early career researchers and activists - Denisse Albornoz  and Antoinette Foster. They reflect on how the values of openness, equity, safety, accountability and much more have influenced and informed their work and career trajectories both in academia and beyond.